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Automation
Skill

/define-criteria

Derive explicit evaluation criteria from the research objective and candidate set.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-criteria --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/define-criteria

Context preview

The summary Claude sees to decide when to auto-load this skill.

Derive explicit evaluation criteria from the research objective and candidate set.

SKILL.md

define-criteria.SKILL.md
name: define-criteria
description: "Derive explicit evaluation criteria from the research objective and candidate set."

define-criteria

Purpose

Derive explicit evaluation criteria from the research objective and candidate set.

Input contract

required: [research_objective, candidate_set, decision_context]
optional: [stakeholder_priorities, measurement_constraints, candidate_domains]
constraints: [criteria must be mutually interpretable across candidates]

Procedure

1. Extract desired outcomes and constraints from the objective. 2. Translate them into candidate-discriminating criteria with definitions and units. 3. Check completeness, overlap, direction, and measurability. 4. Return the criterion schema and unresolved measurement questions.

Output contract

produces: [criterion_schema, measurement_definitions, direction_labels, coverage_notes]
delta_fields: [findings, open_questions, uncertainties]

Quality gates

  • Criteria count is between 3 and 12 unless caller explicitly authorizes another range.
  • Every criterion has name, definition, unit, and higher/lower-is-better direction.
  • Criteria are non-overlapping enough that double counting is documented.

Parameterization

Caller supplies objective schema, candidate schema, criterion count bounds, measurement units, direction vocabulary, and overlap policy.

Failure and counterexamples

Reject vague criteria lacking an observable measurement or criteria that cannot distinguish any candidate.

Provenance map

  • resolved: criterion-definition
  • concept: hypothesis-formation/scoring-matrix-construction (criteria-extraction core)
  • resolved: hypothesis-formation-scoring-matrix-construction
  • intermediate: Pass4/define-success-criteria

Preserved source criteria ledger

| source | criterion | |---|---| | criterion-definition | Criteria count is between 3-12. | | criterion-definition | Each criterion includes name, definition, unit of measurement, and direction (higher-is-better/lower-is-better). | | convergence-scoring-matrix-construction | Normalization method matches the aggregation method. | | convergence-scoring-matrix-construction | Sensitivity testing perturbs at least 3 weight parameters by +/-10%. |

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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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